{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征探索分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   pregnants                     768 non-null    int64  \n",
      " 1   Plasma_glucose_concentration  768 non-null    int64  \n",
      " 2   blood_pressure                768 non-null    int64  \n",
      " 3   Triceps_skin_fold_thickness   768 non-null    int64  \n",
      " 4   serum_insulin                 768 non-null    int64  \n",
      " 5   BMI                           768 non-null    float64\n",
      " 6   Diabetes_pedigree_function    768 non-null    float64\n",
      " 7   Age                           768 non-null    int64  \n",
      " 8   Target                        768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29464078610>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x294656ee730>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>serum_insulin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>543.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serum_insulin  serum_insulin_Missing\n",
       "0            NaN                      1\n",
       "1            NaN                      1\n",
       "2            NaN                      1\n",
       "3           94.0                      0\n",
       "4          168.0                      0\n",
       "5            NaN                      1\n",
       "6           88.0                      0\n",
       "7            NaN                      1\n",
       "8          543.0                      0\n",
       "9            NaN                      1"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理缺失数据\n",
    "train['Triceps_skin_fold_thickness_Missing'] = train['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train['serum_insulin_Missing'] = train['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)\n",
    "train[['serum_insulin','serum_insulin_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2946599d4c0>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对比数据缺失对结果的影响\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "sns.countplot(x=\"Triceps_skin_fold_thickness_Missing\", hue=\"Target\",data=train)\n",
    "sns.countplot(x=\"serum_insulin_Missing\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#使用中值填充缺失数据\n",
    "train.drop([\"Triceps_skin_fold_thickness_Missing\", \"serum_insulin_Missing\"], axis=1, inplace=True)\n",
    "medians = train.median() \n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  get labels\n",
    "y_train = train['Target']   \n",
    "X_train = train.drop([\"Target\"], axis=1)\n",
    "\n",
    "#用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "存储特征处理结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#存为csv格式\n",
    "X_train = pd.DataFrame(columns = feat_names, data = X_train)\n",
    "\n",
    "train = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "train.to_csv('FE_pima-indians-diabetes.csv',index = False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   pregnants                     768 non-null    float64\n",
      " 1   Plasma_glucose_concentration  768 non-null    float64\n",
      " 2   blood_pressure                768 non-null    float64\n",
      " 3   Triceps_skin_fold_thickness   768 non-null    float64\n",
      " 4   serum_insulin                 768 non-null    float64\n",
      " 5   BMI                           768 non-null    float64\n",
      " 6   Diabetes_pedigree_function    768 non-null    float64\n",
      " 7   Age                           768 non-null    float64\n",
      " 8   Target                        768 non-null    int64  \n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_log = pd.read_csv(\"FE_pima-indians-diabetes.csv\")\n",
    "train_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   pregnants                     768 non-null    float64\n",
      " 1   Plasma_glucose_concentration  768 non-null    float64\n",
      " 2   blood_pressure                768 non-null    float64\n",
      " 3   Triceps_skin_fold_thickness   768 non-null    float64\n",
      " 4   serum_insulin                 768 non-null    float64\n",
      " 5   BMI                           768 non-null    float64\n",
      " 6   Diabetes_pedigree_function    768 non-null    float64\n",
      " 7   Age                           768 non-null    float64\n",
      " 8   Target                        768 non-null    int64  \n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train_log.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 默认参数的Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_log['Target']   \n",
    "X_train = train_log.drop([\"Target\"], axis=1)\n",
    "\n",
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns \n",
    "\n",
    "#sklearn的学习器大多之一稀疏数据输入，模型训练会快很多\n",
    "#查看一个学习器是否支持稀疏数据，可以看fit函数是否支持: X: {array-like, sparse matrix}.\n",
    "#可自行用timeit比较稠密数据和稀疏数据的训练时间\n",
    "from scipy.sparse import csr_matrix\n",
    "X_train = csr_matrix(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.48805562 0.53007069 0.45629158 0.42247552 0.48388972]\n",
      "cv logloss is: 0.47615662604739944\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "#%timeit loss_sparse = cross_val_score(lr, X_train_sparse, y_train, cv=5, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', -loss.mean() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.75974026 0.74675325 0.78571429 0.79738562 0.77124183]\n",
      "cv accuracy is: 0.7721670486376369\n"
     ]
    }
   ],
   "source": [
    "loss_acc = cross_val_score(lr, X_train, y_train, cv=5, scoring='accuracy')\n",
    "print ('logloss of each fold is: ',loss_acc)\n",
    "print ('cv accuracy is:', loss_acc.mean() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 正则化的 Logistic Regression及利用log似然损失进行正则超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=777, shuffle=True),\n",
       "             error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='liblinear',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=4,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "                         'penalty': ['l1', 'l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=777)\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='neg_log_loss',n_jobs = 4,return_train_score = True)\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47770567532960717\n",
      "{'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 正则化的 Logistic Regression及利用正确率进行正则超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7707325354384178\n",
      "{'C': 0.1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "#  lr_penalty 复用之前的参数\n",
    "grid_2= GridSearchCV(lr_penalty, tuned_parameters,cv=fold, scoring='accuracy',n_jobs = 4,return_train_score = True) #正确率\n",
    "grid_2.fit(X_train,y_train)\n",
    "print(grid_2.best_score_)\n",
    "print(grid_2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means_log = grid_2.cv_results_[ 'mean_test_score' ]\n",
    "test_stds_log = grid_2.cv_results_[ 'std_test_score' ]\n",
    "train_means_log = grid_2.cv_results_[ 'mean_train_score' ]\n",
    "train_stds_log = grid_2.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds_log = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds_log = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
